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Android Application Framework is an integral and foundational part of the Android system. Each of the two billion (as of 2017) Android devices relies on the system services of Android Framework to manage applications and system resources. Given its critical role, a vulnerability in the framework can be exploited to launch large-scale cyber attacks and cause severe harms to user security and privacy. Recently, many vulnerabilities in Android Framework were exposed, showing that it is indeed vulnerable and exploitable. While there is a large body of studies on Android application analysis, research on Android Framework analysis is very limited. In particular, to our knowledge, there is no prior work that investigates how to enable symbolic execution of the framework, an approach that has proven to be very powerful for vulnerability discovery and exploit generation. We design and build the first system, Centaur , that enables symbolic execution of Android Framework. Due to the middleware nature and technical peculiarities of the framework that impinge on the analysis, many unique challenges arise and are addressed in Centaur . The system has been applied to discovering new vulnerability instances, which can be exploited by recently uncovered attacks against the framework, and to generating PoC exploits.

Ejector refrigeration cycle (ERC) with advantages of simple structure and low cost holds great application potential in cascade/hybrid cycles to improve the overall system performance by removing or recovering the heat from the main cycle. In this paper, a theoretical and experimental investigation of the ERC as a part of a cascade system was carried out. The operating parameters were optimized. The experimental ERC test rig was designed, developed and investigated at high evaporating temperatures and wide ranges of operating conditions. The influence of operating conditions on the efficiency of the ejector and ERC was analyzed. Experimental results and analysis in this study can be helpful for the application and operating condition optimization of ERC in cascade/hybrid refrigeration systems.

Graph Neural Networks (GNNs) has been widely used to address the sparsity and cold start problems in recommendation system. By propagating embeddings from multi-hop neighbor nodes among the interaction graph and update target user and item embeddings, GNNs-based methods can achieve better recommendation performance. But those methods directly concatenate the output of each layer and ignore the different influences between different layers, and they simply use the inner product of the user and item’s embeddings to calculate the similarity and make recommendation based on it, which is insufficient to reveal the complex and nonlinear interactions.In this work, we propose to learn multi-order interactions between users and items and capture correlations between different-order information. We design a new recommendation framework MCCR, which treats each layer’s output as differentorder feature, and propose a multi-order interaction module to represent feature interactions. We adopt a multi-layer 3D CNN module to learn high-order interaction signals between users and items in an explicit approach. Through extensive experiments on three real-world datasets, which shows that MCCR evidently outperforms the state-of-the-art methods consistently.

Since the master key length becomes longer and longer in ciphers, an adversary often needs to preform the multi-round side channel analysis (SCA) in order to recover the master key by enough round keys. Traditional multi-round SCA is launched by adaptive manner in practice, which means that the input of each round is calculated in an on-the-fly way based on all round keys of anterior rounds. However, compared to the classical single-round SCA, the multi-round SCA in adaptive manner is severely limited in several practical scenarios, because all round keys of anterior rounds must be properly recovered before the attack against the next round. In this paper, we focus on the Feistel-SP networks, break the interdependency between the alternating measurement and analysis phases, propose a Multi-round non-Adaptive Chosen Message (MACM) approach, which can reduce the multi-round SCA to the single-round attack. In MACM, the set of plaintexts applied to multiple rounds is calculated in an off-line way. We also prove that the revealed round keys by MACM are adequate to recover the master key. Furthermore, we carefully analyze the advantages of MACM regarding to robustness and compatibility. In order to further manifest the validity of MACM, we perform extensive experiments on three typical Feistel-SP ciphers, Camellia, CLEFIA and SM4, the master keys are recovered as expected, and the number of traces in MACM is at least 25% less than that in the adaptive manner.

This paper presents a novel triple heat sources-driven absorption heat pump system. The exhaust gas is used as the heat source for a high-pressure generator. The superheated refrigerant vapor generated by the high-pressure generator is used for heating the solution in the low-pressure generator 1 by releasing the latent and sensible heat. The low-temperature solar thermal energy serves as the heat source of the low-pressure generator 2. The geothermal energy supplies heat to the evaporator. Heat discharged from the condenser and the absorber is supplied to the air conditioner heat medium water. The high- and low-pressure generators are coupled to each other through an ejector. The introduction of the ejector reduces the pressure of the low pressure generator 2 below the condensing pressure so that the lower temperature solar thermal energy can be utilized. An experimental setup for this heat pump cycle is built, and an experimental investigation is carried out. The influences of the independent variables on the key internal operating parameters and heating performance have been investigated. Experimental studies show that when the hot air temperature is 213 °C and the temperature of the hot water is 43.5 °C, a heating capacity of 26.6 kW can be achieved with a heating performance coefficient greater than 1.7. In this way, an efficient and full use of waste heat is realized, and the results can provide an important basis for the design and wide application of low-temperature solar thermal and geothermal energy.

Embedding knowledge graphs (KGs) into continuous vector space is an essential problem in knowledge extraction. Current models continue to improve embedding by focusing on discriminating relation-specific information from entities with increasingly complex feature engineering. We noted that they ignored the inherent relevance between relations and tried to learn unique discriminate parameter set for each relation. Thus, these models potentially suffer from high time complexity and large parameters, preventing them from efficiently applying on real-world KGs. In this paper, we follow the thought of parameter sharing to simultaneously learn more expressive features, reduce parameters and avoid complex feature engineering. Based on gate structure from LSTM, we propose a novel model TransGate and develop shared discriminate mechanism, resulting in almost same space complexity as indiscriminate models. Furthermore, to develop a more effective and scalable model, we reconstruct the gate with weight vectors making our method has comparative time complexity against indiscriminate model. We conduct extensive experiments on link prediction and triplets classification. Experiments show that TransGate not only outperforms state-of-art baselines, but also reduces parameters greatly. For example, TransGate outperforms ConvE and RGCN with 6x and 17x fewer parameters, respectively. These results indicate that parameter sharing is a superior way to further optimize embedding and TransGate finds a better trade-off between complexity and expressivity.

The blooming of live comment videos leads to the need of automatic live video comment generating task. Previous works focus on autoregressive live video comments generation and can only generate comments by giving the first word of the target comment. However, in some scenes, users need to generate comments by their given prompt keywords, which can't be solved by the traditional live video comment generation methods. In this paper, we propose a Transformer based non-autoregressive conditional masked live video comments generation model called CMVCG model. Our model considers not only the visual and textual context of the comments, but also time and color information. To predict the position of the given prompt keywords, we also introduce a keywords position predicting module. By leveraging the conditional masked language model, our model achieves non-autoregressive live video comment generation. Furthermore, we collect and introduce a large-scale real-world live video comment dataset called Bili-22 dataset. We evaluate our model in two live comment datasets and the experiment results present that our model outperforms the state-of-the-art models in most of the metrics.

Obtaining demographic attributes of online users is of great significance for retail marketing, targeted advertisement and many other scenarios. Users' wanderings on various websites and applications contains user preference on different items, and can be leveraged to infer one's private attributes. Existing studies usually focus on manually defined features, relationships in online social networks, or modeling global user preferences. However, attribute inference from the most common behavioral data (e.g., browsing history, shopping cart) is recently overlooked, and still requires further research. In this work, we propose a Topic-Enhanced Attentive Recurrent Neural Network (TEA-RNN) model to capture both local neighborhood-based features (with attentive RNN) and global patterns (with topic model) within user behaviors, and apply multi-task learning mechanism with weighted losses to further leverage the latent relationships within demographics. Experimental results on real-world datasets demonstrates the effectiveness of TEA-RNN by comparing with several commonly used baselines.

In recent years, attribute-based access control (ABAC) is more and more popular because of its flexibility and fine-grained data management. However, manually configuring authorization policies in ABAC system is a time-consuming, labor-intensive, and tedious work. Many researchers explore the ways of automatically configuring authorization policies by parsing requirement specifications that are expressed in natural language. Previous works only focus on English and ignore the semantics of comparative relationship. In this paper, we propose a method based on Chinese including procedures of key words extraction, tag alignment and expression transformation. It can parse Chinese sentence into constraint expressions and authorization sign, by which ABAC system is able to configure authorization policies automatically. Our evaluation results show that it has good performance not only in the independent tests on each procedure but also in the systematic tests on the whole method.

As a typical application of decentralization, federated learning prevents privacy leakage of crowdsourcing data for various training tasks. Instead of transmitting actual data, federated learning only updates model parameters of server by learning multiple sub-models from clients. However, these parameters may be leaked during transmission and further used by attackers to restore client data. Existing technologies used to protect parameters from privacy leakage do not achieve the sufficient protection of parameter information. In this paper, we propose a novel and efficient privacy protection method, which perturbs the privacy information contained in the parameters and completes its ciphertext representation in transmission. Regarding to the perturbation part, differential privacy is utilized to perturb the real parameters, which can minimize the privacy information contained in the parameters. To further camouflage the parameters, the weak encryption keeps the ciphertext form of the parameters as they are transmitted from the client to the server. As a result, neither the server nor any middle attacker can obtain the real information of the parameter directly. The experiments show that our method effectively resists attacks from both malicious clients and malicious server.

Machine comprehension of texts often requires external common knowledge and coreference resolution in the passage. However, most current machine reading comprehension models only incorporate external common knowledge. We propose CoSp model, which incorporates both common knowledge and specific entity linking knowledge for machine reading comprehension. It employs an attention mechanism to adaptively select relevant commonsense and lexical common knowledge from knowledge bases, then it leverages the relational-GCN for reasoning on the entity graph, which is constructed by the entity coreference and co-occurrence for each passage. Hence we obtain knowledge-aware and coreference-aware contextual word representation for answer prediction. Experimental results indicate that CoSp model offers significant and consistent improvements over BERT, outperforming competitive knowledge-aware models on ReCoRD and SQuAD1.1 benchmarks.

Predicting users' preferences based on historical interactions plays a critical role in recommender systems. Conventionally, users' recent sequence behaviors are fed into a model to predict the next item that a user is most interested in. However, most of the existing methods simply utilize the user's recent behavior sequences to model short-term preferences. This is problematic, because users may pay more attention to partial features in the sequences due to their long-term preferences. To this end, we propose a user Preference Excitation network to more accurately model user sequence information. Specifically, a personalized gating layer is utilized to model the user's specific preferences and a preference excitation self-attention layer learns the complicated sequential patterns. Furthermore, the gate mechanism is used to adaptively fuse the information from these two aspects. Empirically, we evaluate our model on several real-world datasets and the comprehensive experimental results show that our model consistently outperforms the selective state-of-the-art methods. Source code of the paper is available at https://github.com/strange-jiong/PERec.

Traditional attributed network embedding methods are designed to map structural and attribute information of networks jointly into a continuous Euclidean space, while recently a novel branch of them named binarized attributed network embedding has emerged to learn binary codes in Hamming space, aiming to save time and memory costs and to naturally fit node retrieval task. However, current binarized attributed network embedding methods are scarce and mostly ignore the local attribute similarity between each pair of nodes. Besides, none of them attempt to control the independency of each dimension(bit) of the learned binary representation vectors. As existing methods still need improving, we propose an unsupervised Neural-based Binarized Attributed Network Embedding (NBANE) approach. Firstly, we inherit the Weisfeiler-Lehman proximity matrix from predecessors to aggregate high-order features for each node. Secondly, we feed the aggregated features into an autoencoder with the attribute similarity penalizing term and the orthogonality term to make further dimension reduction. To solve the problem of integer optimization we adopt the relaxation-quantization method during the process of training neural networks. Empirically, we evaluate the performance of NBANE through node classification and clustering tasks on three real-world datasets and study a case on fast retrieval in academic networks. Our method achieves better performance over state- of-the-art baselines methods of various types.

User Alignment is to find users belonging to a same real person on different social networks and has become a fundamental task for many sequent applications such as cross-network recommendation systems. When matching users in multiple social networks, existing approaches always know some correctly matched users, which can be called seeds. Then, existing methods strongly depend on the neighboring users of each user to propagate alignment information from seeds and align probable matching users implicitly. However, the completeness and validity of original alignment information among seeds cannot be fully preserved when learning and aligning multiple user spaces. In this paper, we propose a unified framework named Jumping Seed Alignment Information Propagation (JSAIP) to flexibly leverage, for each user, complete and correct alignment information from seeds. Specifically, JSAIP learns a reasonable user space for each social network by preserving enough original network and label information. Then, JSAIP ensures the correct alignment among seeds and shared labels to reduce the diversity between different user spaces. Finally, JSAIP constructs jumping links from seeds to each user in each social network and ultilizes original seed alignment information to enhance or rectify the alignment information propagated from neighbors. Experiments on real world datasets demonstrate the effectiveness of our proposed JSAIP method compared to several state-of-the-art methods.

With the popularity of location-based social networks (LBSNs), Point-of-Interest (POI) recommendation has become an essential location-based service to help people explore novel locations. Although the massive check-in data bring a good opportunity, there are still many challenges in building personalized POI recommender systems based on geographical information. First, current coarse-grained geographical models provide considerably limited improvements on POI recommendations and fail to capture the overall impact of fine-grained geographical characteristics in LBSNs. Second, previous methods such as matrix factorization always give equal weight to each positive example and may not distinguish between their different contributions in learning the objective function. To cope with these challenges, we develop a fine-grained POI recommendation framework that makes full use of the geographical characteristics from both users' and locations' perspectives. For capturing the fine-grained geographical influence, we present a unified probability distribution model based on four key geographical characteristics. For mining more contribution information from positive examples, we assign a higher weight to highlight the contribution of a higher check-in frequency by employing a logistic matrix factorization. Finally, experimental results on two real-world datasets demonstrate the effectiveness and superiority of the proposed method.

Users’ real demographic attributes is impressively useful for intelligent marketing, automatic advertising and human-computer interaction. Traditional method on attribute prediction make great effort on the study of social network data, but ignore massive volumes of disparate, dynamic, and temporal data derived from ubiquitous mobile and sensor devices. For example, daily walking step counts produced by pedometer. Multiple demographic prediction on temporal data have two problems. First one is that differential effectiveness of different time period data for prediction is unclear. And another one is how to effectively learn the complementary correlations between different attributes. To address the above problem, we propose a novel model named Correlation-Aware Neural Embedding with Attention (CANEA), which first directly separates different attribute oriented feature using separated embedding layer, and use attention mechanism to assign a higher weight to dominant time point. Then it captures informative correlations using correlation learning layer. Finally we obtain the refined task-specific representations with optimal correlation information for predicting certain attributes. Experimental results show the effectiveness of our method.

A large-scale knowledge base can support a large number of practical applications, such as intelligent search and intelligent question answering. As the completeness of the information in a knowledge base may have a direct impact on the quality of downstream applications, its automatic completion has become a crucial task for many researchers and practitioners. To address this challenge, the knowledge representation learning technology which represents entities and relations as low-dimensional dense real value vectors has been developed rapidly in recent years. Although researchers continue to improve knowledge representation learning models using an increasingly complex feature engineering, we find that the most advanced models can be outdone by simply considering interactions from entities to relations and that from relations to entities without requiring huge number of parameters. In this work, we present a knowledge embedding model based on a bidirectional filter called TransBidiFilter. By learning the global shared parameter set based on the traditional gate structure, TransBidiFilter captures the restriction rules from entities to relations and that from relations to entities respectively. It achieves better automatic completion ability by modifying the standard translation-based loss function. In doing so, though with much fewer discriminate parameters, TransBidiFilter performs better than state-of-the-art baselines of semantic discriminate models on most indicators on many datasets.

There is a significant style overfitting problem in neural-based character recognition: insufficient generalization ability to recognize characters with unseen styles. To address this problem, we propose a novel framework named Style-Melt Nets (SMN), which disentangles the style and content factors to extract pure content feature. In this framework, a pair of parallel style net and content net is designed to respectively infer the style labels and content labels of input character images, and the style feature produced by the style net is fed to the content net for eliminating the style influence on content feature. In addition, the marginal distribution of character pixels is considered as an important structure indicator for enhancing the content representations. Furthermore, to increase the style diversity of training data, an efficient data augmentation approach for changing the thickness of the strokes and generating outline characters is presented. Extensive experimental results demonstrate the benefit of our methods, and the proposed SMN is able to achieve the state-ofthe-art performance on multiple real world character sets.

With the popularity of smart devices such as mobile phones and tablets, the security problem of the widely used ARMv8-A processor has received more and more attention. Flush+Reload and Flush+Flush cache attacks have become two of the most important security threats due to their low noise and high resolution. In order to resist Flush+Reload and Flush+Flush attacks, researchers proposed many defense methods. However, these existing methods have various shortcomings. The runtime defense methods using hardware performance counters cannot detect attacks fast enough, effectively detect Flush+Flush or avoid a high false positive rate. Static code analysis schemes are powerless for obfuscation techniques. The approaches of permanently reducing the resolution can only be utilized on browser products and cannot be applied in the system. In this paper, we design two more secure collaborative APIs—flush operation API and high resolution time API—which can resist Flush+Reload and Flush+Flush attacks. When the flush operation API is called, the high resolution time API temporarily reduces its resolution and automatically restores. Moreover, the flush operation API also has the ability to detect and handle suspected Flush+Reload and Flush+Flush attacks. The attack and performance comparison experiments prove that the two APIs we designed are safer and the performance losses are acceptable.

ARM-FPGA embedded SoCs have been widely used in the fields of drones, embedded and IoT devices due to its high performance and hardware design flexibility. However, ARM-FPGA embedded SoC suffers various types of security threats, one of which is flush-based cache attack. The proposed defense schemes either lead to a high false positive rate or a large performance loss. Due to the importance of high resolution time APIs in the system, schemes that permanently reduce the resolution of time APIs can only be implemented in specific applications such as browsers. Moreover, the method of protecting high resolution timers in software cannot defend against an attacker with root privileges. In this paper, we propose a more secure timer which is a hardware/software co-design on ARM-FPGA embedded SoC. When a software process calls the flush operation, the timer adaptively reduces its resolution and recover after a short period of time. In the case that the flush operation is not called, the impact of the timer on system performance is almost negligible. This hardware/software co-design guarantees the availability of a high resolution time API while defend against attackers with root privileges. The results of the attack experiments show that the success rates of Flush+Reload and flush-based Spectre attacks can be reduced to less than 1% when using the timer. Performance test results show that the timer access latency is 9.5% slower than the fastest PMCCNTR but 5% faster than the global timer of Cortex-A9 MPCore. The modified flush operation API for the design only increases the time consumption by about 12%.

Big data brings much convenience to our daily life, while it also raises concerns to personal information leakage. It is important for data owner to protect sensitive data from being attacked. In this paper, we elaborate on the possible attacks that may be suffered at various stages of personal data life cycle and the corresponding privacy protection methods. Based on these methods, a differential attribute desensitization system (DADS) for personal information protection has been proposed. Data owner can define sensitive attribute level in the DADS. And then DADS can automatically identify sensitive data, and take differentiated data desensitization measures for structured and unstructured multi-attribute information. Experimental results show that DADS can automatically adapt to different types of sensitive information and protect personal sensitive information effectively.

Knowing individual real attributes is beneficial to intelligent marking, automatic advertising as well as a new application of smart home system. In this paper, we leverage seemingly innocent user information (step count) coming from ubiquitous mobile and sensing device for attribute inference. The utilization of sensor data breaks the traditional dependence on text, social relationship and online behavior in social media, and avoids the problem of users' gender variation in linguistic style on different social networks. Existing methods on attribute inference usually ignore the temporal information of data and purely explore different types of features manually. However, loss of temporal characteristic may reduce the representation ability to infer user's attributes. Meanwhile, excessively depending on manually defined features requires a great mass of human labor and often suffers from under specification. To address these problems, we propose a novel Hybrid Multiple Representations (HMR) model that combines simple human knowledge with automated deep learning by using stacked bidirectional LSTM, Bag-of-step and Holiday Activation methods to predict gender and age from users daily step count. Experiments are conducted on a real-world pedometer dataset where gender and age are to be predicted. The empirical results show that our HMR model does a good job on the task of predicting attributes compared with state-of-the-art baselines.

Recently, network embedding has attracted a surge of attention due to its ability to automatically extract features from graph-structured data. Though network embedding method has been intensively studied, most of the existing approaches pay attention to either structures or attributes. In this paper, we propose a novel attributed network embedding method based on a Siamese neural network, named SANE, to capture both the network structure and node attribute information in a principled way. Specifically, to preserve local semantic proximity, we adopt a Siamese neural network, which can directly learn the similarity of paired nodes with their attributes as input. Then, a skip-gram module is connected with the final shared hidden layer to capture high-order proximity based on the latent representation of node attributes. Thus, we can learn the complex interrelations between nodes. Empirically, we evaluate our model on several real-world datasets and the experimental results have verified the effectiveness of our proposed approach.

This paper addresses attributed network embedding which maps the structural information and multi-modal attribute data into a latent space. Most existing network embedding algorithms concentrate on either node-oriented modeling or edge-oriented modeling, resulting in unilaterally capturing information from nodes or edges. However, there is no effective method to bilaterally extract node attributes cooperated with edge attributes, which delineates the outline and detail of social network. To this end, we propose a novel Node-Edge Bilateral Attributed Network Embedding method named NEBANE. Regarding each edge as a specific node, we construct a pioneering node-edge-node triangular structure for bilateral information modeling on both nodes and edges. Furthermore, we envisage a pairwise loss which maximizes the likelihood of connected node pairs and of connected node-edge pairs to measure the node-node and node-edge similarity. Empirically, experiments on two real-world datasets, including link prediction and node classification, are conducted in this paper. Our method achieves substantial performance gains compared with state-of-the-art baselines (e.g., 4.21%–13.65% lift by AUC scores for link prediction).

User Identity Linkage is to find users belonging to the same real person in different social networks. Besides, anchor users refer to matching users known in advance. However, how to match users only based on network information is still very difficult and existing embedding methods suffer from the challenge of error propagation. Error propagation means the error occurring in learning some users’ embeddings may be propagated and amplified to other users along with edges in the network. In this paper, we propose the Anchor UseR ORiented Accordant Embedding (AURORAE) method to learn the vector representation for each user in each social network by capturing useful network information and avoiding error propagation. Specifically, AURORAE learns the potential relations between anchor users and all users, which means each user is directly connected to all anchor users and the error cannot be propagated without paths. Then, AURORAE captures the useful local structure information into final embeddings under the constraint of accordant vector representations between anchor users. Experimental results on real-world datasets demonstrate that our method significantly outperforms other state-of-the-art methods.

There is a significant style overfitting problem in traditional content supervision models of character recognition: insufficient generalization ability to recognize the characters with unseen font styles. To overcome this problem, in this paper we propose a novel framework named Style and Content Supervision (SCS) network, which integrates style and content supervision to resist style overfitting. Different from traditional models only supervised by content labels, SCS simultaneously leverages the style and content supervision to separate the task-specific features of style and content, and then mixes the style-specific and content-specific features using bilinear model to capture the hidden correlation between them. Experimental results prove that the proposed model is able to achieve the state-of-the-art performance on several widely used real world character sets, and it obtains relatively strong robustness when the size of training set is shrinking.

Knowledge representation learning aims to transform entities and relationships in a knowledge base into computable forms, so that an efficient calculation can be realized. It is of great significance to the construction, reasoning and application of knowledge base. The traditional translation-based models mainly obtain the finite dimension vector representation of entities or relationships by projecting to finite dimensional Euclidean space. These simple and effective methods greatly improve the efficiency and accuracy of knowledge representation. However, they ignore a fact that the semantic space develops and grows forever with the passage of time. Finite dimensional Euclidean space is not enough in capacity for vectorizing infinitely growing semantic space. Time is moving forward forever, so knowledge base would expand infinitely with time. This determines that the vector representation of entities and relationships should support infinite capacity. We fill the gap by putting forward TransI (Translating Infinite Dimensional Embeddings) model, which extends knowledge representation learning from finite dimensions to infinite dimensions. It is trained by Trend Smooth Distance based on the idea of continuous infinite dimension vector representation. The Training Efficiency of TransI model is obviously better than TransE under the same setting, and its effect of Dimension Reduction Clustering is more obvious.

Image steganography is the technique of hiding information within images in plain sight. With the rapid development of deep learning in the field of steganalysis, it becomes a tremendous challenge to design a secure steganographic algorithm. To this end, we propose a novel steganographic network based on style transfer, named STNet. This network accepts the content and style images as input to synthesize art image with content from the former and style from the latter and embeds the secret information in style features. It can effectively resist most steganalysis tools. Steganalysis can identify stego images from cover images, but they cannot distinguish our stego images from other art images. Meanwhile, our method produces stego images of arbitrary size with 0.06 bit per pixel, improving over other deep steganographic models which only can embed fixed-length secret. Experiment results demonstrate that our STNet can achieve great visual effect, security, and reliability.

In recent years, deep neural networks have been widely applied to recommender systems. Although there are extensive explorations of deep neural networks on the collaborative filtering problem in item recommendation, most of the existing methods employ a similar loss function, i.e., the prediction loss of user-item interactions, and only change the form of the input, which may limit the model’s performance. To address this problem, we present a novel framework, named DCAR, short for Deep Collaborative Autoencoder for Recommendation. Specifically, with the implicit feedback matrix as the input, we employ the autoencoder module to obtain the latent representations of users and items respectively. Then, to predict the matching score of corresponding user-item pairs, an interaction prediction module is designed based on the neural network architecture. The two parts are coupled together and employ alternating training to learn. We conduct extensive experiments on several real-world datasets and the results empirically verify the superior performance of DCAR on item recommendation. The code related to this paper is available at: https://github.com/strange-jiong/DCAR.

Aligning users belonging to the same person in different social networks has attracted much attention. Recently, embedding methods have been proposed to represent users from different social networks into vector spaces with same dimension. To handle the challenge of vector space diversity, existing methods usually make vectors of known aligned users closer/consistent and overlap different vector spaces. However, compared to large amount of users in each social network, the consistence constraint on aligned users is not enough to reduce the diversity. Besides, missing edges/labels may provide incorrect information and affect the effect of the overlap between learned vector spaces. Therefore, we propose the OURLACER method, i.e, jOint UseR and LAbel ConsistencE Representation, to jointly represent each user and label under the consistence constraints of know aligned users and shared labels. Specifically, OURLACER utilizes collective matrix factorization to complete missing edges and labels for each user, which can provide sufficient information to distinguish each user. Moreover, OURLACER adds the consistence constraint on shared labels in different social networks. Because each user has own labels, label consistence can restrict each user and greatly reduce the diversity between learned vector spaces. Extensive experiments conducted on real-world datasets demonstrate that our method significantly outperforms other state-of-the-art methods.

Attributed network embedding, which aims to map the structural and attribute information into a latent vector space jointly, has attracted a surge of research attention in recent years. However, a vast majority of existing work explores the correlation between node structure and attribute values whereas the attribute type information which can be potentially complementary is ignored. How to effectively model the nodes, attribute types and attribute values as well as their relations in a unified framework is an open yet challenging problem. To this end, we propose a translation-based attributed network embedding method named TransANE. In our approach, the whole attributed network is considered as a coupled network which consists of two components, i.e., node relation network and attribute correlation network. We construct attribute correlation network by the co-occurrence of attribute values. Each node-attribute relation is regarded as an attributional triple, e.g., (Tom, Gender, Male). We introduce knowledge representation method to model the mapping between nodes, attribute types and attribute values. Empirically, experiments on two real-world datasets including node multi-class classification and network visualization are conducted to evaluate the effectiveness of our method TransANE in this paper. Our method achieves significant performance compared with state-of-the-art baselines.

Public key infrastructures (PKIs) are proposed to provide various security services. Some security services such as confidentiality require key escrow in certain scenarios, whereas some others such as non-repudiation and authentication usually prohibit key escrow. Moreover, these two conflicting requirements can coexist for one PKI user. The popular solution in which each user has two different certificates and an escrow authority backs up all escrowed private keys faces the problems of efficiency and scalability. In this study, a novel key management infrastructure called RIKE+ is proposed to integrate the ‘inherent key escrow’ of identity-based encryption (IBE) into PKIs. In RIKE+ , (the hash value of) a user's PKI certificate also serves as a ‘revocable identity’ to derive the user's IBE public key, and the revocation of this IBE key pair is achieved by the certificate revocation of PKIs. Therefore the certificate binds the user with two key pairs, one of which is escrowed inherently and the other is not. Furthermore, RIKE+ employs chameleon hash to flexibly control the relationship between the certificate and the IBE key pair. In the case of certificate renewal and revocation, chameleon hash enables RIKE+ to manipulate the hash value of the new certificate, so the user's IBE key pair is not unconditionally changed unless it is necessary. RIKE+ is an effective certificate-based solution compatible with traditional PKIs and can be built on existing X.509 PKIs.

Network representation learning (NRL), which has become an focus of current research, learns low-dimensional vertex representations to capture network information. However, conventional NRL models either largely neglect the rich semantic information on edges and fail to extract good features of relations, or employ complex models that have rather high space and time complexities. In this work, we present an efficient NRL model, MultNet, for Social Relation Extraction (SRE) task, which evaluates the ability of NRL models on modeling the relationships between vertices. We conduct extensive experiments on several public data sets and experiments on SRE indicate that MultNet outperforms other baseline models significantly.

Societal event forecasting plays a significant role in crisis warning and emergency management. Most traditional prediction methods focus on predicting whether specific events would happen or not. However, the results of these methods are not always informative for the policy makers due to excessive frequency, lack of details and supportive evidence about the predictive events. In this paper, we focus on the problem of integrated fine-grained event forecasting which is to predict the attributes of events and find out related precursors. Given a collection of news sequences, we transform the problem into a Multi-Instance Multi-Label learning (MIML) framework. Considering the sequential influence of events and hybridity of news, we implement the MIML framework based on the Long Short-Term Memory (LSTM) neural network, and propose the model called MIML-LSTM to extract three levels of deep features which represent news article, daily status and news sequence respectively. Based on this hierarchical representation, we design a compositional objective function for joint training of each part. Taking multiple types of protest event prediction as a demonstration, we evaluate the proposed model on news streams from three countries in Latin America, and the experimental results show the effectiveness of our model on integrated fine-grained event forecasting.

Steganography is designed to conceal a secret message within public media. Traditional steganography needs a lot of expert knowledge and complex artificial rules. To solve this problem, we propose a novel self-learning steganographic algorithm based on the generative adversarial network, which we called SSteGAN. This method learns the steganographic algorithm in an unsupervised manner without expert knowledge and directly generates the stego image from the secret message without the cover image. We define a game with four parts: Alice, Bob, Dev and Eve. Alice and Bob attempt to communicate securely. Eve eavesdrops on their conversation and wants to distinguish whether the secret message is embedded in the image. Dev attempts to determine real images from generated images. Experiment results demonstrate that Alice can produce vivid stego images and Bob can successfully decode the secret message with \( 98.8\% \) accuracy.

Recently, the convolutional neural networks (CNNs) show the great power in dealing with various image classification tasks. However, in the task of Chinese character recognition, there is a significant problem in CNN-based classifiers: insufficient generalization ability to recognize the Chinese characters with unfamiliar font styles. We call this problem the Style Overfitting. In the process of a human recognizing Chinese characters with various font styles, the internal skeletons of these characters are important indicators. This paper proposes a novel tool named Skeleton Kernel to capture skeleton features of Chinese characters. And we use it to assist CNN-based classifiers to prevent the Style Overfitting problem. Experimental results prove that our method firmly enhances the generalization ability of CNN-based classifiers. And compared to previous works, our method requires a small training set to achieve relatively better performance.

Attributed network embedding, which aims to map structural and attribute information into a latent vector space jointly, has attracted a surge of research attention in recent years. However, existing methods mostly concentrate on either the local proximity (i.e., the pairwise similarity of connected nodes) or the global proximity (e.g., the similarity of nodes’ correlation in a global perspective). How to learn the global and local information in structure and attribute into a same latent space simultaneously is an open yet challenging problem. To this end, we propose a Neural-based Attributed Network Embedding (NANE) approach. Firstly, an affinity matrix and an adjacency matrix are introduced to encode the attribute and structural information in terms of the overall picture separately. Then, we impose a neural-based framework with a pairwise constraint to learn the vector representation for each node. Specifically, an explicit loss function is designed to preserve the local and global similarity jointly. Empirically, we evaluate the performance of NANE through node classification and clustering tasks on three real-world datasets. Our method achieves significant performance compared with state-of-the-art baselines.

Sensitive data (e.g., passwords, health data and private videos) can be leaked due to many reasons, including (1) the misuse of legitimate operating system (OS) functions such as core dump, swap and hibernation, and (2) physical attacks to the DRAM chip such as cold-boot attacks and DMA attacks. While existing software-based memory encryption is effective in defeating physical attacks, none of them can prevent a legitimate OS function from accidentally leaking sensitive data in the memory. This paper introduces CryptMe that integrates memory encryption and ARM TrustZone-based memory access controls to protect sensitive data against both attacks. CryptMe essentially extends the Linux kernel with the ability to accommodate the execution of unmodified programs in an isolated execution domain (to defeat OS function misuse), and at the same time transparently encrypt sensitive data appeared in the DRAM chip (to defeat physical attacks). We have conducted extensive experiments on our prototype implementation. The evaluation results show the efficiency and added security of our design.

With the rapid development of IoT devices in the direction of multifunction and personalization, All Programmable SoC has been used more and more frequently because of its unrivaled levels of system performance, flexibility, and scalability. On the other hand, this type of SoC faces a growing range of security threats. Among these threats, cache timing attacks and power/elctromagnetic analysis attacks are two considerable ones which have been widely studied. Although many countermeasures have been proposed to resist these two types of attacks, most of them can only withstand a single type but are often incapable when facing multi-type attacks. In this paper, we utilize the special architecture of All Programmable SoC to implement a secure AES encryption scheme which can efficiently resist both cache timing and power/electromagnetic analysis attacks. The AES implementation has a beginning software stage, a middle hardware stage and a final software stage. Operations in software and start/end round of hardware are all randomized, which allow our implementation to withstand two types of attacks. To illustrate the security of the implementation, we conduct the three types of attacks on unprotected software/hardware AES, shuffled software AES and our scheme. Furthermore, we use Test Vector Leakage Assessment (TVLA) to test their security on encryption times and power/electromagnetic traces. The final result indicates that our encryption implementation achieves a high secure level with almost 0.86 times data throughput of the shuffled software AES implementation.

Summary Background Commercially available tissue engineered skin remains elusive despite extensive research because the multi-stratified anisotropic structure is difficult to replicate in vitro using traditional tissue engineering techniques. Bioprinting, involving computer-controlled deposition of cells and scaffolds into spatially controlled patterns, is able to control not only the macro but also micro and nanoarchitecture and could offer the potential to more faithfully replicate native skin. Methods We conducted a literature review using PubMed, EMBASE and Web of Science for studies on skin 3D bioprinting between 2009 and 2016, evaluating the bioprinting technique, cell source, scaffold type and in vitro and in vivo outcomes. Results We outline the evolution of biological skin replacements, principles of bioprinting and how they apply to the skin tissue engineering field, potential clinical applications as well the current limitations and future avenues for research. Of the studies analysed, the most common types of bioinks consisted of keratinocytes and fibroblasts combined with collagen, although stem cells are gaining increasing recognition. Laser assisted deposition was the most common printing modality, although ink-jet and pneumatic extrusion have also been tested. Bioprinted skin promoted accelerated wound healing, was able to mimic stratified epidermis but not the thick, elastic, vascular dermis. Conclusions Although 3D bioprinting shows promise in engineering skin, evidenced by large collective investments from the cosmetic industry, the research is still in its infancy. The resolution, vascularity, optimal cell and scaffold combinations and cost of bioprinted skin are hurdles that need to be overcome before the clinical applicability can be realised. Small scale 3D skin tissue models for cosmetics, drug and toxicity testing as well as tumour modelling are likely to be translated first before we see this technology used in reconstructive surgery patients.

2,3,3,3-Tetrafluoropropene (R1234yf) is an environmentally friendly promising alternative to 1,1,1,2-tetrafluoroethane (R134a) in air conditioning applications. The heat capacity of R1234yf in liquid phase was investigated in this study. The measurements were carried out with a modified heat conduction calorimeter. A total of 74 data points were obtained among 305–355 K and 1.5–5 MPa. The uncertainty of the heat capacity measurements was estimated to be less than 1.7%. An empirical equation was correlated to reproduce the experimental result with its terms chosen by a stepwise method. The maximum deviation between the correlating equation and the experimental data was 1.5%. Furthermore, saturated liquid heat capacity of R1234yf was obtained by extrapolating the correlation to saturated pressures. Finally, a comparison was done between the present data and literature experimental data and calculations from a fundamental equation of state.

The key whitening is a technique intended to enhance the strength of a block cipher. Although some research work involves DPA attacks against the key whitening layer in the compact architecture, there are no literatures dedicated in the influence of the key whitening layers in the loop architecture from the standpoint of DPA. In this paper, we propose a practical chosen message power analysis approach against the loop architecture of ciphers with the key whitening layers, thus proving that the key whitening technique does not enhance the security of ciphers regard to DPA. Our approach follows a reduction strategy: we recover the whitening key in the general cipher with the key whitening layer and reduce other complicated key whitening layers to the general case. In order to further manifest the validity of the new approach, we carry extensive experiments on two ISO standardized ciphers CLEFIA and Camellia implemented in loop architecture on FPGA, and the keys are recovered as expected.

In this paper, risk acceptability evaluation and risk weight and improved risk level were added in original risk matrix method according to its shortcomings and practicality and need of evaluation of investment risk in international engineering project. A model based on improved risk matrix method was put forward to evaluate investment risk in international engineering project. Firstly, investment risk evaluation index system of international engineering project was established. Secondly, level of risk factors were decided by risk impact level and risk probability scope, borda ordinal value was calculated to form judgment matrix, analytic hierarchy process was introduced to calculate risk weight. Synthetic risk level was determined by product of quantized value of risk level and corresponding risk weight. Risk acceptability evaluation was decided by risk impact level and risk probability scope. Finally, a case study was presented to verify the feasibility of the model which would provide a reliable basis for the investment decision.

Dual-Rail Precharge Logic (DPL) has proven to be an effective countermeasure logic style against Differential Power Analysis (DPA). All previous DPL architectures employ the precharge mechanism to achieve DPA resistance. However, due to its additional precharge phase, an inherent drawback of these DPL architectures lies within its degraded performance (less than 1/2 times compared to the nominal data rate), and hence they are not suitable for applications where high performance is required. In this paper, we present Quadruple-Rail Logic (QRL), a new DPA-hardened approach for cryptographic implementations in FPGA. The main merit of this proposal is that the system throughput can be effectively maintained by removing the precharge phase. By introducing a synchronized and identical quadruple-rail network, strengthened DPA resistance can be achieved. In order to test the robustness of QRL against DPA, we launch DPA on a QRL-based standard AES processor on Xilinx Virtex-5 FPGA. The experimental results show that DPA on QRL AES is failed by analyzing 100,000 power consumption traces, which achieves the competitive DPA resistance level as typical DPL schemes, and gains at least 110 times stronger than the unprotected AES.

Although the input validation vulnerabilities play a critical role in web application security, such vulnerabilities are so far largely neglected in the Android security research community. We found that due to the unique Framework Code layer, Android devices do need specific input validation vulnerability analysis in system services. In this work, we take the first steps to analyze Android specific input validation vulnerabilities. In particular, a) we take the first steps towards measuring the corresponding attack surface and reporting the current input validation status of Android system services. b) We developed a new input validation vulnerability scanner for Android devices. This tool fuzzes all the Android system services by sending requests with malformed arguments to them. Through comprehensive evaluation of Android system with over 90 system services and over 1,900 system service methods, we identified 16 vulnerabilities in Android system services. We have reported all the issues to Google and Google has confirmed them.

To protect sensitive data from unauthorized access, encrypting data at the user end before outsourcing them to the cloud storage, has become a common practice. In this case, the access control policy is enforced through assigning proper cryptographic keys among collaborators. However, when the access control policy needs to be updated (e.g. new collaborators join or some collaborators leave), it is very costly for the data owner or other parties to re-encrypt the data with a new key in order to satisfy the new policy. To address this problem, we propose a dual-header structure and batch revocation, which makes the overhead for privileges grant independent of data size and significantly improves the efficiency of privilege revocation by applying lazy revocation to certain groups of revocation requests, respectively. We also analyze the overhead for authorization showing that our approach is able to efficiently manage frequent policy updates.

Smartphones are playing an increasingly important role in personal life and carrying massive private data. Unfortunately, once the smartphones are stolen, all the sensitive information, such as contacts, messages, photos, credit card information and passwords, may fall into the hands of malicious people. In order to protect the private data, remote deletion mechanism is required to allow owners to wipe the sensitive data on the stolen phone remotely. Existing remote deletion techniques rely on the availability of either WiFi for Internet connection or SIM card for cellular network connection; however, these requirements may not be satisfied when the phones are stolen by some sophisticated adversaries. In this paper, we propose a new remote deletion mechanism that allows the phone owner to delete the private data remotely even if the WiFi is disabled and the SIM card is unplugged. The basic idea is to use emergency call mechanisms to establish a communication connection with a service provider to verify the state of the phone and perform remote deletion. We present a case study of our mechanism with the Universal Mobile Telecommunications System (UMTS) network.